Overview

Dataset statistics

Number of variables42
Number of observations12973
Missing cells51056
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory245.0 B

Variable types

Numeric17
Text4
Categorical15
DateTime6

Alerts

id_de_parliament is highly overall correlated with elecper and 4 other fieldsHigh correlation
elecper is highly overall correlated with id_de_parliament and 4 other fieldsHigh correlation
year_birth is highly overall correlated with id_de_parliament and 3 other fieldsHigh correlation
district_id is highly overall correlated with listHigh correlation
districtvote is highly overall correlated with closeness_district and 2 other fieldsHigh correlation
closeness_district is highly overall correlated with districtvoteHigh correlation
listpos is highly overall correlated with listpos_totalHigh correlation
listpos_total is highly overall correlated with listposHigh correlation
elecsafe_district is highly overall correlated with districtvote and 2 other fieldsHigh correlation
elecsafe_list is highly overall correlated with elecsafe_overall and 2 other fieldsHigh correlation
elecsafe_overall is highly overall correlated with elecsafe_listHigh correlation
partyid_cmp is highly overall correlated with partyid_parlgov and 4 other fieldsHigh correlation
partyid_ches is highly overall correlated with party_elec and 3 other fieldsHigh correlation
partyid_parlgov is highly overall correlated with partyid_cmp and 4 other fieldsHigh correlation
partyid_parlgov2 is highly overall correlated with partyid_cmp and 4 other fieldsHigh correlation
id_de_parliament_string is highly overall correlated with id_de_parliament and 10 other fieldsHigh correlation
mp_id_old is highly overall correlated with id_de_parliament and 3 other fieldsHigh correlation
gender is highly overall correlated with id_de_parliament_stringHigh correlation
party_elec is highly overall correlated with partyid_cmp and 6 other fieldsHigh correlation
party_elecdet is highly overall correlated with partyid_cmp and 6 other fieldsHigh correlation
mandate is highly overall correlated with elecsafe_district and 4 other fieldsHigh correlation
mandate_detailed is highly overall correlated with elecsafe_district and 2 other fieldsHigh correlation
dualcand is highly overall correlated with elecsafe_list and 1 other fieldsHigh correlation
list is highly overall correlated with district_id and 1 other fieldsHigh correlation
partyid_bl is highly overall correlated with id_de_parliament and 8 other fieldsHigh correlation
office_spell is highly imbalanced (76.5%)Imbalance
minister is highly imbalanced (79.8%)Imbalance
junminister is highly imbalanced (76.9%)Imbalance
parlpres is highly imbalanced (91.9%)Imbalance
commchair is highly imbalanced (56.8%)Imbalance
ppgchair is highly imbalanced (68.2%)Imbalance
whip is highly imbalanced (81.2%)Imbalance
district_id has 1463 (11.3%) missing valuesMissing
districtvote has 6595 (50.8%) missing valuesMissing
closeness_district has 1463 (11.3%) missing valuesMissing
list has 2195 (16.9%) missing valuesMissing
listpos has 2511 (19.4%) missing valuesMissing
listpos_total has 2623 (20.2%) missing valuesMissing
elecsafe_district has 1782 (13.7%) missing valuesMissing
elecsafe_list has 1782 (13.7%) missing valuesMissing
elecsafe_overall has 1782 (13.7%) missing valuesMissing
partyid_ches has 8462 (65.2%) missing valuesMissing
partyid_bl has 12320 (95.0%) missing valuesMissing
partyid_parlgov2 has 6299 (48.6%) missing valuesMissing
mp_id_old has 1735 (13.4%) missing valuesMissing
id_de_parliament is highly skewed (γ1 = 81.98301697)Skewed
districtvote is highly skewed (γ1 = 54.62250718)Skewed
id_de_parliament_string is highly skewed (γ1 = 81.98301462)Skewed
elecsafe_district has 807 (6.2%) zerosZeros
elecsafe_list has 1695 (13.1%) zerosZeros

Reproduction

Analysis started2023-11-09 19:46:42.180207
Analysis finished2023-11-09 19:50:32.338601
Duration3 minutes and 50.16 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id_de_parliament
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4098
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11009738
Minimum11000001
Maximum66666664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:32.502986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11000001
5-th percentile11000197
Q111000958
median11001888
Q311002808
95-th percentile11004297
Maximum66666664
Range55666663
Interquartile range (IQR)1850

Descriptive statistics

Standard deviation625963.25
Coefficient of variation (CV)0.056855415
Kurtosis6795.3149
Mean11009738
Median Absolute Deviation (MAD)926
Skewness81.983017
Sum1.4282934 × 1011
Variance3.9182998 × 1011
MonotonicityNot monotonic
2023-11-09T20:50:32.719415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001938 18
 
0.1%
11002531 17
 
0.1%
11001012 17
 
0.1%
11001512 16
 
0.1%
11000102 15
 
0.1%
11002525 14
 
0.1%
11001849 14
 
0.1%
11002270 14
 
0.1%
11000570 14
 
0.1%
11002444 13
 
0.1%
Other values (4088) 12821
98.8%
ValueCountFrequency (%)
11000001 7
0.1%
11000002 6
< 0.1%
11000003 3
< 0.1%
11000004 2
 
< 0.1%
11000005 5
< 0.1%
11000007 4
< 0.1%
11000008 1
 
< 0.1%
11000009 7
0.1%
11000010 4
< 0.1%
11000011 4
< 0.1%
ValueCountFrequency (%)
66666664 1
< 0.1%
55555556 1
< 0.1%
11004972 1
< 0.1%
11004971 1
< 0.1%
11004970 1
< 0.1%
11004969 1
< 0.1%
11004968 1
< 0.1%
11004967 1
< 0.1%
11004966 1
< 0.1%
11004962 1
< 0.1%
Distinct3433
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:33.170740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length27
Mean length7.315116
Min length2

Characters and Unicode

Total characters94899
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1005 ?
Unique (%)7.7%

Sample

1st rowAbelein
2nd rowAbelein
3rd rowAbelein
4th rowAbelein
5th rowAbelein
ValueCountFrequency (%)
schmidt 135
 
1.0%
mueller 120
 
0.9%
fischer 53
 
0.4%
schroeder 40
 
0.3%
schneider 40
 
0.3%
schaefer 36
 
0.3%
vogel 34
 
0.3%
jahn 34
 
0.3%
becker 33
 
0.3%
neumann 33
 
0.3%
Other values (3438) 12567
95.7%
2023-11-09T20:50:33.719750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14491
15.3%
r 8542
 
9.0%
n 6654
 
7.0%
a 5466
 
5.8%
i 4918
 
5.2%
l 4900
 
5.2%
h 4395
 
4.6%
t 4137
 
4.4%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (54) 34386
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80546
84.9%
Uppercase Letter 13590
 
14.3%
Dash Punctuation 481
 
0.5%
Space Separator 164
 
0.2%
Open Punctuation 59
 
0.1%
Close Punctuation 59
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14491
18.0%
r 8542
10.6%
n 6654
 
8.3%
a 5466
 
6.8%
i 4918
 
6.1%
l 4900
 
6.1%
h 4395
 
5.5%
t 4137
 
5.1%
s 3680
 
4.6%
c 3330
 
4.1%
Other values (23) 20033
24.9%
Uppercase Letter
ValueCountFrequency (%)
S 2015
14.8%
B 1344
9.9%
H 1206
 
8.9%
K 1108
 
8.2%
M 959
 
7.1%
W 950
 
7.0%
L 755
 
5.6%
R 724
 
5.3%
G 720
 
5.3%
F 536
 
3.9%
Other values (15) 3273
24.1%
Open Punctuation
ValueCountFrequency (%)
( 58
98.3%
[ 1
 
1.7%
Close Punctuation
ValueCountFrequency (%)
) 58
98.3%
] 1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 481
100.0%
Space Separator
ValueCountFrequency (%)
164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94136
99.2%
Common 763
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14491
15.4%
r 8542
 
9.1%
n 6654
 
7.1%
a 5466
 
5.8%
i 4918
 
5.2%
l 4900
 
5.2%
h 4395
 
4.7%
t 4137
 
4.4%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (48) 33623
35.7%
Common
ValueCountFrequency (%)
- 481
63.0%
164
 
21.5%
( 58
 
7.6%
) 58
 
7.6%
[ 1
 
0.1%
] 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94610
99.7%
None 289
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14491
15.3%
r 8542
 
9.0%
n 6654
 
7.0%
a 5466
 
5.8%
i 4918
 
5.2%
l 4900
 
5.2%
h 4395
 
4.6%
t 4137
 
4.4%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (46) 34097
36.0%
None
ValueCountFrequency (%)
ö 103
35.6%
ü 103
35.6%
ä 32
 
11.1%
ß 27
 
9.3%
Ö 8
 
2.8%
é 8
 
2.8%
è 5
 
1.7%
ÄŸ 3
 
1.0%
Distinct1473
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:34.119589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length34
Mean length7.1354351
Min length3

Characters and Unicode

Total characters92568
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)3.8%

Sample

1st rowManfred
2nd rowManfred
3rd rowManfred
4th rowManfred
5th rowManfred
ValueCountFrequency (%)
hans 328
 
2.3%
peter 321
 
2.2%
karl 314
 
2.2%
wolfgang 276
 
1.9%
hermann 250
 
1.7%
josef 217
 
1.5%
heinrich 210
 
1.4%
franz 188
 
1.3%
klaus 186
 
1.3%
wilhelm 185
 
1.3%
Other values (985) 12018
82.9%
2023-11-09T20:50:34.729757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9764
 
10.5%
r 9655
 
10.4%
a 8152
 
8.8%
n 6695
 
7.2%
i 6180
 
6.7%
t 5034
 
5.4%
l 4962
 
5.4%
o 3621
 
3.9%
s 3202
 
3.5%
h 3166
 
3.4%
Other values (52) 32137
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75074
81.1%
Uppercase Letter 15155
 
16.4%
Space Separator 1527
 
1.6%
Dash Punctuation 660
 
0.7%
Other Punctuation 86
 
0.1%
Open Punctuation 33
 
< 0.1%
Close Punctuation 33
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9764
13.0%
r 9655
12.9%
a 8152
10.9%
n 6695
8.9%
i 6180
8.2%
t 5034
 
6.7%
l 4962
 
6.6%
o 3621
 
4.8%
s 3202
 
4.3%
h 3166
 
4.2%
Other values (19) 14643
19.5%
Uppercase Letter
ValueCountFrequency (%)
H 2222
14.7%
W 1149
 
7.6%
K 1091
 
7.2%
A 1047
 
6.9%
M 1027
 
6.8%
J 971
 
6.4%
G 954
 
6.3%
E 924
 
6.1%
R 775
 
5.1%
F 715
 
4.7%
Other values (16) 4280
28.2%
Dash Punctuation
ValueCountFrequency (%)
- 657
99.5%
– 3
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 84
97.7%
" 2
 
2.3%
Space Separator
ValueCountFrequency (%)
1527
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90229
97.5%
Common 2339
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9764
 
10.8%
r 9655
 
10.7%
a 8152
 
9.0%
n 6695
 
7.4%
i 6180
 
6.8%
t 5034
 
5.6%
l 4962
 
5.5%
o 3621
 
4.0%
s 3202
 
3.5%
h 3166
 
3.5%
Other values (45) 29798
33.0%
Common
ValueCountFrequency (%)
1527
65.3%
- 657
28.1%
. 84
 
3.6%
( 33
 
1.4%
) 33
 
1.4%
– 3
 
0.1%
" 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92492
99.9%
None 73
 
0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9764
 
10.6%
r 9655
 
10.4%
a 8152
 
8.8%
n 6695
 
7.2%
i 6180
 
6.7%
t 5034
 
5.4%
l 4962
 
5.4%
o 3621
 
3.9%
s 3202
 
3.5%
h 3166
 
3.4%
Other values (46) 32061
34.7%
None
ValueCountFrequency (%)
ü 25
34.2%
ö 25
34.2%
ä 12
16.4%
é 10
 
13.7%
Ö 1
 
1.4%
Punctuation
ValueCountFrequency (%)
– 3
100.0%

elecper
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.560163
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size630.1 KiB
2023-11-09T20:50:34.894139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4863684
Coefficient of variation (CV)0.51953442
Kurtosis-1.1755436
Mean10.560163
Median Absolute Deviation (MAD)5
Skewness-0.11084286
Sum136997
Variance30.100238
MonotonicityNot monotonic
2023-11-09T20:50:35.012973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 933
 
7.2%
12 805
 
6.2%
18 802
 
6.2%
11 775
 
6.0%
14 768
 
5.9%
13 723
 
5.6%
16 680
 
5.2%
9 677
 
5.2%
17 674
 
5.2%
15 654
 
5.0%
Other values (9) 5482
42.3%
ValueCountFrequency (%)
1 556
4.3%
2 621
4.8%
3 596
4.6%
4 641
4.9%
5 637
4.9%
6 588
4.5%
7 614
4.7%
8 597
4.6%
9 677
5.2%
10 632
4.9%
ValueCountFrequency (%)
19 933
7.2%
18 802
6.2%
17 674
5.2%
16 680
5.2%
15 654
5.0%
14 768
5.9%
13 723
5.6%
12 805
6.2%
11 775
6.0%
10 632
4.9%

gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
male
10427 
female
2546 

Length

Max length6
Median length4
Mean length4.3925075
Min length4

Characters and Unicode

Total characters56984
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 10427
80.4%
female 2546
 
19.6%

Length

2023-11-09T20:50:35.166577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:35.312805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 10427
80.4%
female 2546
 
19.6%

Most occurring characters

ValueCountFrequency (%)
e 15519
27.2%
m 12973
22.8%
a 12973
22.8%
l 12973
22.8%
f 2546
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56984
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15519
27.2%
m 12973
22.8%
a 12973
22.8%
l 12973
22.8%
f 2546
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 56984
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15519
27.2%
m 12973
22.8%
a 12973
22.8%
l 12973
22.8%
f 2546
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15519
27.2%
m 12973
22.8%
a 12973
22.8%
l 12973
22.8%
f 2546
 
4.5%

year_birth
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1935.9913
Minimum1875
Maximum1992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-09T20:50:35.445899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1875
5-th percentile1897
Q11920
median1939
Q31952
95-th percentile1972
Maximum1992
Range117
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.696016
Coefficient of variation (CV)0.011723202
Kurtosis-0.64221048
Mean1935.9913
Median Absolute Deviation (MAD)16
Skewness-0.17601542
Sum25115615
Variance515.10916
MonotonicityNot monotonic
2023-11-09T20:50:35.609327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1943 331
 
2.6%
1942 283
 
2.2%
1949 270
 
2.1%
1944 265
 
2.0%
1940 256
 
2.0%
1939 255
 
2.0%
1941 247
 
1.9%
1929 242
 
1.9%
1952 232
 
1.8%
1936 219
 
1.7%
Other values (108) 10373
80.0%
ValueCountFrequency (%)
1875 1
 
< 0.1%
1876 8
0.1%
1877 1
 
< 0.1%
1878 2
 
< 0.1%
1879 6
< 0.1%
1880 8
0.1%
1881 12
0.1%
1882 3
 
< 0.1%
1883 11
0.1%
1884 14
0.1%
ValueCountFrequency (%)
1992 2
 
< 0.1%
1991 2
 
< 0.1%
1990 3
 
< 0.1%
1989 12
0.1%
1988 2
 
< 0.1%
1987 12
0.1%
1986 14
0.1%
1985 18
0.1%
1984 22
0.2%
1983 22
0.2%
Distinct3861
Distinct (%)29.8%
Missing1
Missing (%)< 0.1%
Memory size718.7 KiB
Minimum1875-12-14 00:00:00
Maximum1992-12-12 00:00:00
2023-11-09T20:50:35.777506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:35.945888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3863
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
Minimum1875-12-14 00:00:00
Maximum1992-12-12 00:00:00
2023-11-09T20:50:36.129791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:36.298936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct685
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
Minimum1949-09-07 00:00:00
Maximum2021-08-19 00:00:00
2023-11-09T20:50:36.479409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:36.654778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct687
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
Minimum1949-09-15 00:00:00
Maximum2021-10-26 00:00:00
2023-11-09T20:50:36.819646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:36.999426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

office_spell
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
1.0
11638 
2.0
 
1155
3.0
 
161
4.0
 
16
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38919
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11638
89.7%
2.0 1155
 
8.9%
3.0 161
 
1.2%
4.0 16
 
0.1%
5.0 3
 
< 0.1%

Length

2023-11-09T20:50:37.149892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:37.298576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11638
89.7%
2.0 1155
 
8.9%
3.0 161
 
1.2%
4.0 16
 
0.1%
5.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25946
66.7%
Other Punctuation 12973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12973
50.0%
1 11638
44.9%
2 1155
 
4.5%
3 161
 
0.6%
4 16
 
0.1%
5 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 12973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%
Distinct1363
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
Minimum1949-09-07 00:00:00
Maximum2021-08-19 00:00:00
2023-11-09T20:50:37.449735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:37.618713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1361
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
Minimum1949-09-12 00:00:00
Maximum2021-10-26 00:00:00
2023-11-09T20:50:37.776277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:37.947729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

party_elec
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size630.4 KiB
SPD
4682 
CDU
4542 
FDP
1223 
CSU
1112 
Greens
636 
Other values (4)
778 

Length

Max length14
Median length3
Mean length3.6591382
Min length3

Characters and Unicode

Total characters47470
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCDU
2nd rowCDU
3rd rowCDU
4th rowCDU
5th rowCDU

Common Values

ValueCountFrequency (%)
SPD 4682
36.1%
CDU 4542
35.0%
FDP 1223
 
9.4%
CSU 1112
 
8.6%
Greens 636
 
4.9%
Left Party/PDS 437
 
3.4%
other party 225
 
1.7%
AfD 112
 
0.9%
unaffiliated 4
 
< 0.1%

Length

2023-11-09T20:50:38.098093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:38.268261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
spd 4682
34.3%
cdu 4542
33.3%
fdp 1223
 
9.0%
csu 1112
 
8.2%
greens 636
 
4.7%
left 437
 
3.2%
party/pds 437
 
3.2%
other 225
 
1.7%
party 225
 
1.7%
afd 112
 
0.8%

Most occurring characters

ValueCountFrequency (%)
D 10996
23.2%
P 6779
14.3%
S 6231
13.1%
C 5654
11.9%
U 5654
11.9%
e 1938
 
4.1%
r 1523
 
3.2%
t 1328
 
2.8%
F 1223
 
2.6%
a 670
 
1.4%
Other values (16) 5474
11.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37722
79.5%
Lowercase Letter 8649
 
18.2%
Space Separator 662
 
1.4%
Other Punctuation 437
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1938
22.4%
r 1523
17.6%
t 1328
15.4%
a 670
 
7.7%
y 662
 
7.7%
n 640
 
7.4%
s 636
 
7.4%
f 557
 
6.4%
o 225
 
2.6%
h 225
 
2.6%
Other values (5) 245
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
D 10996
29.2%
P 6779
18.0%
S 6231
16.5%
C 5654
15.0%
U 5654
15.0%
F 1223
 
3.2%
G 636
 
1.7%
L 437
 
1.2%
A 112
 
0.3%
Space Separator
ValueCountFrequency (%)
662
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46371
97.7%
Common 1099
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 10996
23.7%
P 6779
14.6%
S 6231
13.4%
C 5654
12.2%
U 5654
12.2%
e 1938
 
4.2%
r 1523
 
3.3%
t 1328
 
2.9%
F 1223
 
2.6%
a 670
 
1.4%
Other values (14) 4375
 
9.4%
Common
ValueCountFrequency (%)
662
60.2%
/ 437
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 10996
23.2%
P 6779
14.3%
S 6231
13.1%
C 5654
11.9%
U 5654
11.9%
e 1938
 
4.1%
r 1523
 
3.2%
t 1328
 
2.8%
F 1223
 
2.6%
a 670
 
1.4%
Other values (16) 5474
11.5%

party_elecdet
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size630.8 KiB
SPD (Social Democratic Party of Germany)
4681 
CDU (Christian Democratic Union)
4542 
FDP (Free Democratic Party)
1203 
CSU (Christian Social Union)
1112 
Greens
644 
Other values (14)
791 

Length

Max length61
Median length55
Mean length33.776305
Min length6

Characters and Unicode

Total characters438180
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCDU (Christian Democratic Union)
2nd rowCDU (Christian Democratic Union)
3rd rowCDU (Christian Democratic Union)
4th rowCDU (Christian Democratic Union)
5th rowCDU (Christian Democratic Union)

Common Values

ValueCountFrequency (%)
SPD (Social Democratic Party of Germany) 4681
36.1%
CDU (Christian Democratic Union) 4542
35.0%
FDP (Free Democratic Party) 1203
 
9.3%
CSU (Christian Social Union) 1112
 
8.6%
Greens 644
 
5.0%
Left/PDS (The Left, previously Party of Democratic Socialism) 437
 
3.4%
AfD (Alternative für Deutschland) 109
 
0.8%
DP (German Party) 76
 
0.6%
GB/BHE (All-German Block/Party of Displaced Persons) 38
 
0.3%
BP (Bavarian Party) 31
 
0.2%
Other values (9) 100
 
0.8%

Length

2023-11-09T20:50:38.448751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
democratic 10866
17.8%
party 6503
10.7%
social 5793
9.5%
christian 5656
9.3%
union 5654
9.3%
of 5176
8.5%
germany 4701
7.7%
spd 4681
7.7%
cdu 4542
7.4%
free 1220
 
2.0%
Other values (46) 6240
10.2%

Most occurring characters

ValueCountFrequency (%)
49262
 
11.2%
i 35214
 
8.0%
a 34534
 
7.9%
r 30528
 
7.0%
o 28563
 
6.5%
c 28230
 
6.4%
t 24370
 
5.6%
n 22840
 
5.2%
D 22107
 
5.0%
e 21731
 
5.0%
Other values (39) 140801
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 282936
64.6%
Uppercase Letter 80300
 
18.3%
Space Separator 49262
 
11.2%
Open Punctuation 12326
 
2.8%
Close Punctuation 12326
 
2.8%
Other Punctuation 992
 
0.2%
Dash Punctuation 38
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 35214
12.4%
a 34534
12.2%
r 30528
10.8%
o 28563
10.1%
c 28230
10.0%
t 24370
8.6%
n 22840
8.1%
e 21731
7.7%
m 16185
5.7%
y 11679
 
4.1%
Other values (13) 29062
10.3%
Uppercase Letter
ValueCountFrequency (%)
D 22107
27.5%
P 13071
16.3%
S 12475
15.5%
C 11357
14.1%
U 11308
14.1%
G 5503
 
6.9%
F 2440
 
3.0%
L 893
 
1.1%
T 437
 
0.5%
A 292
 
0.4%
Other values (9) 417
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 536
54.0%
, 437
44.1%
' 19
 
1.9%
Space Separator
ValueCountFrequency (%)
49262
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12326
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12326
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363236
82.9%
Common 74944
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 35214
 
9.7%
a 34534
 
9.5%
r 30528
 
8.4%
o 28563
 
7.9%
c 28230
 
7.8%
t 24370
 
6.7%
n 22840
 
6.3%
D 22107
 
6.1%
e 21731
 
6.0%
m 16185
 
4.5%
Other values (32) 98934
27.2%
Common
ValueCountFrequency (%)
49262
65.7%
( 12326
 
16.4%
) 12326
 
16.4%
/ 536
 
0.7%
, 437
 
0.6%
- 38
 
0.1%
' 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 438071
> 99.9%
None 109
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49262
 
11.2%
i 35214
 
8.0%
a 34534
 
7.9%
r 30528
 
7.0%
o 28563
 
6.5%
c 28230
 
6.4%
t 24370
 
5.6%
n 22840
 
5.2%
D 22107
 
5.0%
e 21731
 
5.0%
Other values (38) 140692
32.1%
None
ValueCountFrequency (%)
ü 109
100.0%

mandate
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
list mandate
7103 
district mandate
5870 

Length

Max length16
Median length12
Mean length13.809913
Min length12

Characters and Unicode

Total characters179156
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdistrict mandate
2nd rowdistrict mandate
3rd rowdistrict mandate
4th rowdistrict mandate
5th rowdistrict mandate

Common Values

ValueCountFrequency (%)
list mandate 7103
54.8%
district mandate 5870
45.2%

Length

2023-11-09T20:50:38.599467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:38.749624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mandate 12973
50.0%
list 7103
27.4%
district 5870
22.6%

Most occurring characters

ValueCountFrequency (%)
t 31816
17.8%
a 25946
14.5%
i 18843
10.5%
d 18843
10.5%
s 12973
7.2%
12973
7.2%
m 12973
7.2%
n 12973
7.2%
e 12973
7.2%
l 7103
 
4.0%
Other values (2) 11740
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166183
92.8%
Space Separator 12973
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 31816
19.1%
a 25946
15.6%
i 18843
11.3%
d 18843
11.3%
s 12973
7.8%
m 12973
7.8%
n 12973
7.8%
e 12973
7.8%
l 7103
 
4.3%
r 5870
 
3.5%
Space Separator
ValueCountFrequency (%)
12973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 166183
92.8%
Common 12973
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 31816
19.1%
a 25946
15.6%
i 18843
11.3%
d 18843
11.3%
s 12973
7.8%
m 12973
7.8%
n 12973
7.8%
e 12973
7.8%
l 7103
 
4.3%
r 5870
 
3.5%
Common
ValueCountFrequency (%)
12973
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 31816
17.8%
a 25946
14.5%
i 18843
10.5%
d 18843
10.5%
s 12973
7.2%
12973
7.2%
m 12973
7.2%
n 12973
7.2%
e 12973
7.2%
l 7103
 
4.0%
Other values (2) 11740
 
6.6%

mandate_detailed
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.3 KiB
list mandate (at time of election)
6231 
district mandate
5856 
replacement mandate from list
727 
elected by GDR parliament (only EP 11)
 
145
by-election (only EP 1)
 
14

Length

Max length38
Median length34
Mean length25.627457
Min length16

Characters and Unicode

Total characters332465
Distinct characters26
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdistrict mandate
2nd rowdistrict mandate
3rd rowdistrict mandate
4th rowdistrict mandate
5th rowdistrict mandate

Common Values

ValueCountFrequency (%)
list mandate (at time of election) 6231
48.0%
district mandate 5856
45.1%
replacement mandate from list 727
 
5.6%
elected by GDR parliament (only EP 11) 145
 
1.1%
by-election (only EP 1) 14
 
0.1%

Length

2023-11-09T20:50:38.879618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:39.028701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mandate 12814
24.1%
list 6958
13.1%
at 6231
11.7%
time 6231
11.7%
of 6231
11.7%
election 6231
11.7%
district 5856
11.0%
replacement 727
 
1.4%
from 727
 
1.4%
only 159
 
0.3%
Other values (8) 912
 
1.7%

Most occurring characters

ValueCountFrequency (%)
t 51208
15.4%
40104
12.1%
e 34296
10.3%
a 32876
9.9%
i 31291
9.4%
m 20644
 
6.2%
n 20090
 
6.0%
d 18815
 
5.7%
l 14379
 
4.3%
o 13362
 
4.0%
Other values (16) 55400
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278510
83.8%
Space Separator 40104
 
12.1%
Open Punctuation 6390
 
1.9%
Close Punctuation 6390
 
1.9%
Uppercase Letter 753
 
0.2%
Decimal Number 304
 
0.1%
Dash Punctuation 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 51208
18.4%
e 34296
12.3%
a 32876
11.8%
i 31291
11.2%
m 20644
7.4%
n 20090
 
7.2%
d 18815
 
6.8%
l 14379
 
5.2%
o 13362
 
4.8%
c 12973
 
4.7%
Other values (6) 28576
10.3%
Uppercase Letter
ValueCountFrequency (%)
E 159
21.1%
P 159
21.1%
G 145
19.3%
D 145
19.3%
R 145
19.3%
Space Separator
ValueCountFrequency (%)
40104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6390
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6390
100.0%
Decimal Number
ValueCountFrequency (%)
1 304
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 279263
84.0%
Common 53202
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 51208
18.3%
e 34296
12.3%
a 32876
11.8%
i 31291
11.2%
m 20644
7.4%
n 20090
 
7.2%
d 18815
 
6.7%
l 14379
 
5.1%
o 13362
 
4.8%
c 12973
 
4.6%
Other values (11) 29329
10.5%
Common
ValueCountFrequency (%)
40104
75.4%
( 6390
 
12.0%
) 6390
 
12.0%
1 304
 
0.6%
- 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 51208
15.4%
40104
12.1%
e 34296
10.3%
a 32876
9.9%
i 31291
9.4%
m 20644
 
6.2%
n 20090
 
6.0%
d 18815
 
5.7%
l 14379
 
4.3%
o 13362
 
4.0%
Other values (16) 55400
16.7%

dualcand
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
yes
9455 
no
3518 

Length

Max length3
Median length3
Mean length2.7288214
Min length2

Characters and Unicode

Total characters35401
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 9455
72.9%
no 3518
 
27.1%

Length

2023-11-09T20:50:39.176015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:39.318506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 9455
72.9%
no 3518
 
27.1%

Most occurring characters

ValueCountFrequency (%)
y 9455
26.7%
e 9455
26.7%
s 9455
26.7%
n 3518
 
9.9%
o 3518
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35401
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 9455
26.7%
e 9455
26.7%
s 9455
26.7%
n 3518
 
9.9%
o 3518
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 35401
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 9455
26.7%
e 9455
26.7%
s 9455
26.7%
n 3518
 
9.9%
o 3518
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 9455
26.7%
e 9455
26.7%
s 9455
26.7%
n 3518
 
9.9%
o 3518
 
9.9%

district_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct328
Distinct (%)2.8%
Missing1463
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean138.49557
Minimum1
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:39.453150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q169
median136
Q3204
95-th percentile277
Maximum328
Range327
Interquartile range (IQR)135

Descriptive statistics

Standard deviation81.866602
Coefficient of variation (CV)0.59111351
Kurtosis-0.97830045
Mean138.49557
Median Absolute Deviation (MAD)67
Skewness0.15492446
Sum1594084
Variance6702.1405
MonotonicityNot monotonic
2023-11-09T20:50:39.612239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188 60
 
0.5%
69 58
 
0.4%
100 57
 
0.4%
140 57
 
0.4%
131 56
 
0.4%
97 56
 
0.4%
10 56
 
0.4%
163 55
 
0.4%
141 55
 
0.4%
78 54
 
0.4%
Other values (318) 10946
84.4%
(Missing) 1463
 
11.3%
ValueCountFrequency (%)
1 36
0.3%
2 36
0.3%
3 41
0.3%
4 40
0.3%
5 42
0.3%
6 38
0.3%
7 53
0.4%
8 40
0.3%
9 42
0.3%
10 56
0.4%
ValueCountFrequency (%)
328 10
0.1%
327 5
< 0.1%
326 5
< 0.1%
325 3
 
< 0.1%
324 6
< 0.1%
323 7
0.1%
322 4
 
< 0.1%
321 3
 
< 0.1%
320 5
< 0.1%
319 9
0.1%

districtvote
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1541
Distinct (%)24.2%
Missing6595
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean0.47831726
Minimum0.030305019
Maximum48.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:39.783412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.030305019
5-th percentile0.1693653
Q10.415
median0.478
Q30.53735728
95-th percentile0.647
Maximum48.9
Range48.869695
Interquartile range (IQR)0.12235728

Descriptive statistics

Standard deviation0.86713661
Coefficient of variation (CV)1.8128901
Kurtosis3049.332
Mean0.47831726
Median Absolute Deviation (MAD)0.061
Skewness54.622507
Sum3050.7075
Variance0.75192591
MonotonicityNot monotonic
2023-11-09T20:50:40.001010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.469 41
 
0.3%
0.488 40
 
0.3%
0.486 39
 
0.3%
0.501 39
 
0.3%
0.477 38
 
0.3%
0.483 38
 
0.3%
0.485 37
 
0.3%
0.49 37
 
0.3%
0.479 37
 
0.3%
0.474 35
 
0.3%
Other values (1531) 5997
46.2%
(Missing) 6595
50.8%
ValueCountFrequency (%)
0.03030501865 1
< 0.1%
0.03500933573 1
< 0.1%
0.03593908623 1
< 0.1%
0.03929926082 1
< 0.1%
0.04234809056 2
< 0.1%
0.04536836222 1
< 0.1%
0.04541448131 1
< 0.1%
0.04542474449 1
< 0.1%
0.04573408887 1
< 0.1%
0.04603046179 2
< 0.1%
ValueCountFrequency (%)
48.9 2
< 0.1%
0.82 1
< 0.1%
0.819 2
< 0.1%
0.802 1
< 0.1%
0.8 1
< 0.1%
0.796 2
< 0.1%
0.794 1
< 0.1%
0.791 1
< 0.1%
0.777 1
< 0.1%
0.772 2
< 0.1%

closeness_district
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5388
Distinct (%)46.8%
Missing1463
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean0.15733776
Minimum0
Maximum0.74558337
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:40.268005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.011869436
Q10.061072528
median0.12996608
Q30.23175901
95-th percentile0.39154502
Maximum0.74558337
Range0.74558337
Interquartile range (IQR)0.17068648

Descriptive statistics

Standard deviation0.12078013
Coefficient of variation (CV)0.76764876
Kurtosis0.54200946
Mean0.15733776
Median Absolute Deviation (MAD)0.079022462
Skewness0.94542883
Sum1810.9576
Variance0.014587841
MonotonicityNot monotonic
2023-11-09T20:50:40.549881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05799999833 13
 
0.1%
0.201000005 12
 
0.1%
0.2649999857 11
 
0.1%
0.04399999976 10
 
0.1%
0.08600000292 10
 
0.1%
0.09300000221 10
 
0.1%
0.003000000026 9
 
0.1%
0.1770000011 9
 
0.1%
0.04100000113 9
 
0.1%
0.1469999999 8
 
0.1%
Other values (5378) 11409
87.9%
(Missing) 1463
 
11.3%
ValueCountFrequency (%)
0 3
< 0.1%
0.0001193295515 2
< 0.1%
0.0001274707415 2
< 0.1%
0.0001554085152 4
< 0.1%
0.0002140987479 1
 
< 0.1%
0.0002601637297 2
< 0.1%
0.0002954295314 3
< 0.1%
0.0003683995088 2
< 0.1%
0.0004321332064 3
< 0.1%
0.0004407349255 2
< 0.1%
ValueCountFrequency (%)
0.7455833727 1
< 0.1%
0.7098927795 1
< 0.1%
0.703929966 2
< 0.1%
0.6995185003 1
< 0.1%
0.6926352573 1
< 0.1%
0.6925889467 1
< 0.1%
0.6844805195 2
< 0.1%
0.6800405336 1
< 0.1%
0.6753564507 1
< 0.1%
0.6523922627 2
< 0.1%

list
Categorical

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.2%
Missing2195
Missing (%)16.9%
Memory size630.8 KiB
Nordrhein-Westfalen
2641 
Bayern
1448 
Niedersachsen
1292 
Baden-Württemberg
1103 
Hessen
1027 
Other values (14)
3267 

Length

Max length36
Median length29
Mean length12.849137
Min length6

Characters and Unicode

Total characters138488
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNordrhein-Westfalen
2nd rowNordrhein-Westfalen
3rd rowNordrhein-Westfalen
4th rowNordrhein-Westfalen
5th rowNordrhein-Westfalen

Common Values

ValueCountFrequency (%)
Nordrhein-Westfalen 2641
20.4%
Bayern 1448
11.2%
Niedersachsen 1292
10.0%
Baden-Württemberg 1103
8.5%
Hessen 1027
 
7.9%
Rheinland-Pfalz 606
 
4.7%
Berlin 514
 
4.0%
Schleswig-Holstein 481
 
3.7%
Hamburg 335
 
2.6%
Sachsen 271
 
2.1%
Other values (9) 1060
8.2%
(Missing) 2195
16.9%

Length

2023-11-09T20:50:40.812101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nordrhein-westfalen 2641
24.2%
bayern 1448
13.3%
niedersachsen 1292
11.8%
baden-württemberg 1103
10.1%
hessen 1027
 
9.4%
rheinland-pfalz 606
 
5.6%
berlin 514
 
4.7%
schleswig-holstein 481
 
4.4%
hamburg 335
 
3.1%
sachsen 271
 
2.5%
Other values (12) 1186
10.9%

Most occurring characters

ValueCountFrequency (%)
e 22288
16.1%
n 14455
 
10.4%
r 12417
 
9.0%
a 9487
 
6.9%
s 8715
 
6.3%
i 6206
 
4.5%
d 6057
 
4.4%
h 5895
 
4.3%
l 5894
 
4.3%
t 5603
 
4.0%
Other values (30) 41471
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116986
84.5%
Uppercase Letter 16056
 
11.6%
Dash Punctuation 5194
 
3.8%
Space Separator 126
 
0.1%
Open Punctuation 42
 
< 0.1%
Decimal Number 42
 
< 0.1%
Close Punctuation 42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22288
19.1%
n 14455
12.4%
r 12417
10.6%
a 9487
8.1%
s 8715
 
7.4%
i 6206
 
5.3%
d 6057
 
5.2%
h 5895
 
5.0%
l 5894
 
5.0%
t 5603
 
4.8%
Other values (13) 19969
17.1%
Uppercase Letter
ValueCountFrequency (%)
N 3933
24.5%
W 3780
23.5%
B 3418
21.3%
H 1850
11.5%
S 1137
 
7.1%
P 648
 
4.0%
R 606
 
3.8%
A 203
 
1.3%
T 191
 
1.2%
M 124
 
0.8%
Other values (2) 166
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 5194
100.0%
Space Separator
ValueCountFrequency (%)
126
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Decimal Number
ValueCountFrequency (%)
1 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 133042
96.1%
Common 5446
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22288
16.8%
n 14455
10.9%
r 12417
 
9.3%
a 9487
 
7.1%
s 8715
 
6.6%
i 6206
 
4.7%
d 6057
 
4.6%
h 5895
 
4.4%
l 5894
 
4.4%
t 5603
 
4.2%
Other values (25) 36025
27.1%
Common
ValueCountFrequency (%)
- 5194
95.4%
126
 
2.3%
( 42
 
0.8%
1 42
 
0.8%
) 42
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137158
99.0%
None 1330
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22288
16.2%
n 14455
 
10.5%
r 12417
 
9.1%
a 9487
 
6.9%
s 8715
 
6.4%
i 6206
 
4.5%
d 6057
 
4.4%
h 5895
 
4.3%
l 5894
 
4.3%
t 5603
 
4.1%
Other values (29) 40141
29.3%
None
ValueCountFrequency (%)
ü 1330
100.0%

listpos
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)0.9%
Missing2511
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean12.20866
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:41.049680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q316
95-th percentile42
Maximum94
Range93
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.629584
Coefficient of variation (CV)1.1163866
Kurtosis5.5459727
Mean12.20866
Median Absolute Deviation (MAD)5
Skewness2.1875291
Sum127727
Variance185.76556
MonotonicityNot monotonic
2023-11-09T20:50:41.229599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1103
 
8.5%
2 969
 
7.5%
3 849
 
6.5%
4 734
 
5.7%
5 636
 
4.9%
6 548
 
4.2%
7 470
 
3.6%
8 420
 
3.2%
9 382
 
2.9%
10 341
 
2.6%
Other values (79) 4010
30.9%
(Missing) 2511
19.4%
ValueCountFrequency (%)
1 1103
8.5%
2 969
7.5%
3 849
6.5%
4 734
5.7%
5 636
4.9%
6 548
4.2%
7 470
3.6%
8 420
 
3.2%
9 382
 
2.9%
10 341
 
2.6%
ValueCountFrequency (%)
94 1
 
< 0.1%
89 2
< 0.1%
88 2
< 0.1%
87 2
< 0.1%
86 3
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
83 1
 
< 0.1%
81 4
< 0.1%
80 4
< 0.1%

listpos_total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)1.0%
Missing2623
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean51.234879
Minimum3
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:41.387776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q128
median47
Q370
95-th percentile115
Maximum152
Range149
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.36002
Coefficient of variation (CV)0.59256546
Kurtosis0.34193372
Mean51.234879
Median Absolute Deviation (MAD)21
Skewness0.7905838
Sum530281
Variance921.7308
MonotonicityNot monotonic
2023-11-09T20:50:41.559764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 314
 
2.4%
89 296
 
2.3%
80 250
 
1.9%
63 248
 
1.9%
40 242
 
1.9%
36 240
 
1.8%
60 222
 
1.7%
30 209
 
1.6%
12 204
 
1.6%
25 201
 
1.5%
Other values (97) 7924
61.1%
(Missing) 2623
 
20.2%
ValueCountFrequency (%)
3 2
 
< 0.1%
4 11
 
0.1%
5 40
 
0.3%
6 79
 
0.6%
7 64
 
0.5%
8 54
 
0.4%
9 58
 
0.4%
10 175
1.3%
11 84
0.6%
12 204
1.6%
ValueCountFrequency (%)
152 47
0.4%
151 47
0.4%
136 30
0.2%
124 34
0.3%
123 66
0.5%
122 28
0.2%
121 35
0.3%
120 45
0.3%
118 51
0.4%
117 51
0.4%

elecsafe_district
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9278
Distinct (%)82.9%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.43916823
Minimum0
Maximum1
Zeros807
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:41.728888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0039665741
median0.35896128
Q30.91455752
95-th percentile0.9993403
Maximum1
Range1
Interquartile range (IQR)0.91059094

Descriptive statistics

Standard deviation0.41475403
Coefficient of variation (CV)0.9444081
Kurtosis-1.6963851
Mean0.43916823
Median Absolute Deviation (MAD)0.35881037
Skewness0.20208889
Sum4914.7316
Variance0.17202091
MonotonicityNot monotonic
2023-11-09T20:50:41.887988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 807
 
6.2%
1 8
 
0.1%
0.003976718057 5
 
< 0.1%
0.05346974358 5
 
< 0.1%
0.9727668166 5
 
< 0.1%
0.9980874658 4
 
< 0.1%
0.002155815251 4
 
< 0.1%
0.9959763288 4
 
< 0.1%
0.0288213715 4
 
< 0.1%
0.9615659118 4
 
< 0.1%
Other values (9268) 10341
79.7%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
0 807
6.2%
6.104203243 × 10-141
 
< 0.1%
3.020422725 × 10-131
 
< 0.1%
1.425204139 × 10-121
 
< 0.1%
3.230485541 × 10-121
 
< 0.1%
4.112940457 × 10-121
 
< 0.1%
4.75785817 × 10-121
 
< 0.1%
9.232057827 × 10-121
 
< 0.1%
9.885444026 × 10-121
 
< 0.1%
1.069227692 × 10-111
 
< 0.1%
ValueCountFrequency (%)
1 8
0.1%
0.9999999404 3
 
< 0.1%
0.9999998808 2
 
< 0.1%
0.9999998212 1
 
< 0.1%
0.9999997616 1
 
< 0.1%
0.999999702 1
 
< 0.1%
0.9999996424 1
 
< 0.1%
0.9999995828 2
 
< 0.1%
0.9999995232 1
 
< 0.1%
0.9999989867 1
 
< 0.1%

elecsafe_list
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7166
Distinct (%)64.0%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.59242183
Minimum0
Maximum1
Zeros1695
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:42.066777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.044417255
median0.80111861
Q30.99900234
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95458508

Descriptive statistics

Standard deviation0.42692509
Coefficient of variation (CV)0.72064376
Kurtosis-1.6267219
Mean0.59242183
Median Absolute Deviation (MAD)0.19888139
Skewness-0.39255357
Sum6629.7927
Variance0.18226503
MonotonicityNot monotonic
2023-11-09T20:50:42.218649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1695
 
13.1%
1 844
 
6.5%
0.9999999404 123
 
0.9%
0.9999998808 63
 
0.5%
0.9999997616 31
 
0.2%
0.999999702 29
 
0.2%
0.9999998212 27
 
0.2%
0.9999995828 19
 
0.1%
0.9999993443 18
 
0.1%
0.9999994636 16
 
0.1%
Other values (7156) 8326
64.2%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
0 1695
13.1%
2.851232855 × 10-221
 
< 0.1%
1.833519175 × 10-201
 
< 0.1%
2.229652158 × 10-191
 
< 0.1%
5.127287816 × 10-191
 
< 0.1%
6.314920135 × 10-191
 
< 0.1%
6.235041785 × 10-181
 
< 0.1%
1.382694763 × 10-171
 
< 0.1%
2.558271938 × 10-171
 
< 0.1%
6.395317127 × 10-171
 
< 0.1%
ValueCountFrequency (%)
1 844
6.5%
0.9999999404 123
 
0.9%
0.9999998808 63
 
0.5%
0.9999998212 27
 
0.2%
0.9999997616 31
 
0.2%
0.999999702 29
 
0.2%
0.9999996424 15
 
0.1%
0.9999995828 19
 
0.1%
0.9999995232 11
 
0.1%
0.9999994636 16
 
0.1%

elecsafe_overall
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8421
Distinct (%)75.2%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.85927793
Minimum1.0863167 × 10-12
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:42.395025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0863167 × 10-12
5-th percentile0.24567127
Q10.82629907
median0.98726839
Q30.9998078
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.17350873

Descriptive statistics

Standard deviation0.24006955
Coefficient of variation (CV)0.27938522
Kurtosis3.0943737
Mean0.85927793
Median Absolute Deviation (MAD)0.012731612
Skewness-1.9851635
Sum9616.1793
Variance0.057633393
MonotonicityNot monotonic
2023-11-09T20:50:42.578663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 936
 
7.2%
0.9999999404 150
 
1.2%
0.9999998808 71
 
0.5%
0.9999997616 34
 
0.3%
0.9999998212 32
 
0.2%
0.999999702 30
 
0.2%
0.9999995828 25
 
0.2%
0.9999996424 19
 
0.1%
0.9999989271 17
 
0.1%
0.9999992847 16
 
0.1%
Other values (8411) 9861
76.0%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
1.086316692 × 10-121
< 0.1%
2.639846952 × 10-101
< 0.1%
8.444628885 × 10-101
< 0.1%
1.027684049 × 10-91
< 0.1%
1.373900105 × 10-91
< 0.1%
3.495790324 × 10-91
< 0.1%
1.447138764 × 10-81
< 0.1%
2.614445904 × 10-81
< 0.1%
1.23955644 × 10-71
< 0.1%
1.857931551 × 10-71
< 0.1%
ValueCountFrequency (%)
1 936
7.2%
0.9999999404 150
 
1.2%
0.9999998808 71
 
0.5%
0.9999998212 32
 
0.2%
0.9999997616 34
 
0.3%
0.999999702 30
 
0.2%
0.9999996424 19
 
0.1%
0.9999995828 25
 
0.2%
0.9999995232 16
 
0.1%
0.9999994636 16
 
0.1%

minister
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
12565 
yes
 
408

Length

Max length3
Median length2
Mean length2.0314499
Min length2

Characters and Unicode

Total characters26354
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 12565
96.9%
yes 408
 
3.1%

Length

2023-11-09T20:50:42.719447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:42.849626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12565
96.9%
yes 408
 
3.1%

Most occurring characters

ValueCountFrequency (%)
n 12565
47.7%
o 12565
47.7%
y 408
 
1.5%
e 408
 
1.5%
s 408
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26354
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12565
47.7%
o 12565
47.7%
y 408
 
1.5%
e 408
 
1.5%
s 408
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 26354
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12565
47.7%
o 12565
47.7%
y 408
 
1.5%
e 408
 
1.5%
s 408
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12565
47.7%
o 12565
47.7%
y 408
 
1.5%
e 408
 
1.5%
s 408
 
1.5%

junminister
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
12486 
yes
 
487

Length

Max length3
Median length2
Mean length2.0375395
Min length2

Characters and Unicode

Total characters26433
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 12486
96.2%
yes 487
 
3.8%

Length

2023-11-09T20:50:42.962663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:43.082754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12486
96.2%
yes 487
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 12486
47.2%
o 12486
47.2%
y 487
 
1.8%
e 487
 
1.8%
s 487
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26433
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12486
47.2%
o 12486
47.2%
y 487
 
1.8%
e 487
 
1.8%
s 487
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 26433
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12486
47.2%
o 12486
47.2%
y 487
 
1.8%
e 487
 
1.8%
s 487
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12486
47.2%
o 12486
47.2%
y 487
 
1.8%
e 487
 
1.8%
s 487
 
1.8%

parlpres
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
12843 
yes
 
130

Length

Max length3
Median length2
Mean length2.0100208
Min length2

Characters and Unicode

Total characters26076
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 12843
99.0%
yes 130
 
1.0%

Length

2023-11-09T20:50:43.628659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:43.762531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12843
99.0%
yes 130
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 12843
49.3%
o 12843
49.3%
y 130
 
0.5%
e 130
 
0.5%
s 130
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26076
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12843
49.3%
o 12843
49.3%
y 130
 
0.5%
e 130
 
0.5%
s 130
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 26076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12843
49.3%
o 12843
49.3%
y 130
 
0.5%
e 130
 
0.5%
s 130
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12843
49.3%
o 12843
49.3%
y 130
 
0.5%
e 130
 
0.5%
s 130
 
0.5%

commchair
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
11822 
yes
 
1151

Length

Max length3
Median length2
Mean length2.0887227
Min length2

Characters and Unicode

Total characters27097
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 11822
91.1%
yes 1151
 
8.9%

Length

2023-11-09T20:50:43.872706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:44.000001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 11822
91.1%
yes 1151
 
8.9%

Most occurring characters

ValueCountFrequency (%)
n 11822
43.6%
o 11822
43.6%
y 1151
 
4.2%
e 1151
 
4.2%
s 1151
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27097
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 11822
43.6%
o 11822
43.6%
y 1151
 
4.2%
e 1151
 
4.2%
s 1151
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 27097
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 11822
43.6%
o 11822
43.6%
y 1151
 
4.2%
e 1151
 
4.2%
s 1151
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 11822
43.6%
o 11822
43.6%
y 1151
 
4.2%
e 1151
 
4.2%
s 1151
 
4.2%

ppgchair
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
12225 
yes
 
748

Length

Max length3
Median length2
Mean length2.0576582
Min length2

Characters and Unicode

Total characters26694
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 12225
94.2%
yes 748
 
5.8%

Length

2023-11-09T20:50:44.112454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:44.238795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12225
94.2%
yes 748
 
5.8%

Most occurring characters

ValueCountFrequency (%)
n 12225
45.8%
o 12225
45.8%
y 748
 
2.8%
e 748
 
2.8%
s 748
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26694
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12225
45.8%
o 12225
45.8%
y 748
 
2.8%
e 748
 
2.8%
s 748
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 26694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12225
45.8%
o 12225
45.8%
y 748
 
2.8%
e 748
 
2.8%
s 748
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12225
45.8%
o 12225
45.8%
y 748
 
2.8%
e 748
 
2.8%
s 748
 
2.8%

whip
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size630.2 KiB
no
12599 
yes
 
374

Length

Max length3
Median length2
Mean length2.0288291
Min length2

Characters and Unicode

Total characters26320
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 12599
97.1%
yes 374
 
2.9%

Length

2023-11-09T20:50:44.344779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:44.468871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 12599
97.1%
yes 374
 
2.9%

Most occurring characters

ValueCountFrequency (%)
n 12599
47.9%
o 12599
47.9%
y 374
 
1.4%
e 374
 
1.4%
s 374
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26320
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 12599
47.9%
o 12599
47.9%
y 374
 
1.4%
e 374
 
1.4%
s 374
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 26320
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 12599
47.9%
o 12599
47.9%
y 374
 
1.4%
e 374
 
1.4%
s 374
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 12599
47.9%
o 12599
47.9%
y 374
 
1.4%
e 374
 
1.4%
s 374
 
1.4%

partyid_cmp
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.1%
Missing33
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean41415.326
Minimum41111
Maximum41953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:44.573346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41111
5-th percentile41220
Q141320
median41420
Q341521
95-th percentile41521
Maximum41953
Range842
Interquartile range (IQR)201

Descriptive statistics

Standard deviation135.56217
Coefficient of variation (CV)0.0032732368
Kurtosis2.0056342
Mean41415.326
Median Absolute Deviation (MAD)101
Skewness0.31235453
Sum5.3591432 × 108
Variance18377.101
MonotonicityNot monotonic
2023-11-09T20:50:44.688930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
41521 5654
43.6%
41320 4682
36.1%
41420 1220
 
9.4%
41113 469
 
3.6%
41222 221
 
1.7%
41111 164
 
1.3%
41221 112
 
0.9%
41953 112
 
0.9%
41223 80
 
0.6%
41620 76
 
0.6%
Other values (8) 150
 
1.2%
ValueCountFrequency (%)
41111 164
 
1.3%
41112 11
 
0.1%
41113 469
 
3.6%
41220 20
 
0.2%
41221 112
 
0.9%
41222 221
 
1.7%
41223 80
 
0.6%
41320 4682
36.1%
41420 1220
 
9.4%
41521 5654
43.6%
ValueCountFrequency (%)
41953 112
 
0.9%
41951 38
 
0.3%
41912 1
 
< 0.1%
41911 31
 
0.2%
41712 6
 
< 0.1%
41711 19
 
0.1%
41620 76
 
0.6%
41522 24
 
0.2%
41521 5654
43.6%
41420 1220
 
9.4%

partyid_ches
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.2%
Missing8462
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean302.9335
Minimum301
Maximum310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:44.799596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile301
Q1301
median302
Q3304
95-th percentile308
Maximum310
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3605237
Coefficient of variation (CV)0.0077922175
Kurtosis1.0486686
Mean302.9335
Median Absolute Deviation (MAD)1
Skewness1.4316275
Sum1366533
Variance5.5720725
MonotonicityNot monotonic
2023-11-09T20:50:44.922976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
302 1473
 
11.4%
301 1430
 
11.0%
304 419
 
3.2%
303 371
 
2.9%
308 356
 
2.7%
306 350
 
2.7%
310 112
 
0.9%
(Missing) 8462
65.2%
ValueCountFrequency (%)
301 1430
11.0%
302 1473
11.4%
303 371
 
2.9%
304 419
 
3.2%
306 350
 
2.7%
308 356
 
2.7%
310 112
 
0.9%
ValueCountFrequency (%)
310 112
 
0.9%
308 356
 
2.7%
306 350
 
2.7%
304 419
 
3.2%
303 371
 
2.9%
302 1473
11.4%
301 1430
11.0%

partyid_bl
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.8%
Missing12320
Missing (%)95.0%
Memory size718.7 KiB
307.0
266 
44.0
266 
135.0
61 
122.0
58 
249.0
 
2

Length

Max length5
Median length5
Mean length4.5926493
Min length4

Characters and Unicode

Total characters2999
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row307.0
2nd row307.0
3rd row307.0
4th row44.0
5th row307.0

Common Values

ValueCountFrequency (%)
307.0 266
 
2.1%
44.0 266
 
2.1%
135.0 61
 
0.5%
122.0 58
 
0.4%
249.0 2
 
< 0.1%
(Missing) 12320
95.0%

Length

2023-11-09T20:50:45.044915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-09T20:50:45.178858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
307.0 266
40.7%
44.0 266
40.7%
135.0 61
 
9.3%
122.0 58
 
8.9%
249.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2346
78.2%
Other Punctuation 653
 
21.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 919
39.2%
4 534
22.8%
3 327
 
13.9%
7 266
 
11.3%
1 119
 
5.1%
2 118
 
5.0%
5 61
 
2.6%
9 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

partyid_parlgov
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1106.2759
Minimum137
Maximum2253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:45.298697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile543
Q1558
median772
Q31727
95-th percentile1727
Maximum2253
Range2116
Interquartile range (IQR)1169

Descriptive statistics

Standard deviation574.51495
Coefficient of variation (CV)0.51932341
Kurtosis-1.8537446
Mean1106.2759
Median Absolute Deviation (MAD)229
Skewness0.1903161
Sum14340654
Variance330067.41
MonotonicityNot monotonic
2023-11-09T20:50:45.394722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1727 5654
43.6%
558 4682
36.1%
543 1220
 
9.4%
772 644
 
5.0%
791 437
 
3.4%
2253 112
 
0.9%
912 76
 
0.6%
1507 38
 
0.3%
1131 31
 
0.2%
137 24
 
0.2%
Other values (3) 45
 
0.3%
ValueCountFrequency (%)
137 24
 
0.2%
187 6
 
< 0.1%
543 1220
 
9.4%
558 4682
36.1%
649 20
 
0.2%
772 644
 
5.0%
791 437
 
3.4%
912 76
 
0.6%
1131 31
 
0.2%
1420 19
 
0.1%
ValueCountFrequency (%)
2253 112
 
0.9%
1727 5654
43.6%
1507 38
 
0.3%
1420 19
 
0.1%
1131 31
 
0.2%
912 76
 
0.6%
791 437
 
3.4%
772 644
 
5.0%
649 20
 
0.2%
558 4682
36.1%

partyid_parlgov2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing6299
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean870.17651
Minimum358
Maximum2253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size668.1 KiB
2023-11-09T20:50:45.508053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum358
5-th percentile558
Q1808
median808
Q3808
95-th percentile1180
Maximum2253
Range1895
Interquartile range (IQR)0

Descriptive statistics

Standard deviation245.48076
Coefficient of variation (CV)0.28210456
Kurtosis14.62143
Mean870.17651
Median Absolute Deviation (MAD)0
Skewness3.1188731
Sum5807558
Variance60260.801
MonotonicityNot monotonic
2023-11-09T20:50:45.619970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
808 4542
35.0%
1180 1112
 
8.6%
558 477
 
3.7%
772 160
 
1.2%
791 159
 
1.2%
2253 112
 
0.9%
543 95
 
0.7%
358 17
 
0.1%
(Missing) 6299
48.6%
ValueCountFrequency (%)
358 17
 
0.1%
543 95
 
0.7%
558 477
 
3.7%
772 160
 
1.2%
791 159
 
1.2%
808 4542
35.0%
1180 1112
 
8.6%
2253 112
 
0.9%
ValueCountFrequency (%)
2253 112
 
0.9%
1180 1112
 
8.6%
808 4542
35.0%
791 159
 
1.2%
772 160
 
1.2%
558 477
 
3.7%
543 95
 
0.7%
358 17
 
0.1%

id_de_parliament_string
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4098
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11009738
Minimum11000001
Maximum66666664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:45.779677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11000001
5-th percentile11000197
Q111000958
median11001888
Q311002808
95-th percentile11004297
Maximum66666664
Range55666663
Interquartile range (IQR)1850

Descriptive statistics

Standard deviation625963.25
Coefficient of variation (CV)0.056855415
Kurtosis6795.3146
Mean11009738
Median Absolute Deviation (MAD)926
Skewness81.983015
Sum1.4282934 × 1011
Variance3.9182999 × 1011
MonotonicityNot monotonic
2023-11-09T20:50:45.929812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001938 18
 
0.1%
11002531 17
 
0.1%
11001012 17
 
0.1%
11001512 16
 
0.1%
11000102 15
 
0.1%
11002525 14
 
0.1%
11001849 14
 
0.1%
11002270 14
 
0.1%
11000570 14
 
0.1%
11002444 13
 
0.1%
Other values (4088) 12821
98.8%
ValueCountFrequency (%)
11000001 7
0.1%
11000002 6
< 0.1%
11000003 3
< 0.1%
11000004 2
 
< 0.1%
11000005 5
< 0.1%
11000007 4
< 0.1%
11000008 1
 
< 0.1%
11000009 7
0.1%
11000010 4
< 0.1%
11000011 4
< 0.1%
ValueCountFrequency (%)
66666664 1
< 0.1%
55555556 1
< 0.1%
11004972 1
< 0.1%
11004971 1
< 0.1%
11004970 1
< 0.1%
11004969 1
< 0.1%
11004968 1
< 0.1%
11004967 1
< 0.1%
11004966 1
< 0.1%
11004962 1
< 0.1%

mp_id_old
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3588
Distinct (%)31.9%
Missing1735
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean66605.833
Minimum10
Maximum182832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:46.089920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1143
Q16171
median71153.5
Q3121739
95-th percentile162679.3
Maximum182832
Range182822
Interquartile range (IQR)115568

Descriptive statistics

Standard deviation60465.146
Coefficient of variation (CV)0.90780557
Kurtosis-1.4593544
Mean66605.833
Median Absolute Deviation (MAD)62589.5
Skewness0.25193154
Sum7.4851635 × 108
Variance3.6560339 × 109
MonotonicityIncreasing
2023-11-09T20:50:46.262455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307 17
 
0.1%
10756 17
 
0.1%
6475 16
 
0.1%
70012 15
 
0.1%
397 15
 
0.1%
9654 14
 
0.1%
2414 14
 
0.1%
10700 14
 
0.1%
9703 13
 
0.1%
7880 13
 
0.1%
Other values (3578) 11090
85.5%
(Missing) 1735
 
13.4%
ValueCountFrequency (%)
10 7
0.1%
14 6
< 0.1%
19 3
< 0.1%
25 4
< 0.1%
26 1
 
< 0.1%
30 7
0.1%
34 4
< 0.1%
37 1
 
< 0.1%
46 1
 
< 0.1%
51 1
 
< 0.1%
ValueCountFrequency (%)
182832 1
 
< 0.1%
182828 1
 
< 0.1%
182803 1
 
< 0.1%
182787 1
 
< 0.1%
182781 3
< 0.1%
182773 3
< 0.1%
182771 1
 
< 0.1%
182734 4
< 0.1%
182733 1
 
< 0.1%
182686 2
< 0.1%
Distinct4072
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:46.549419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length30
Mean length21.833269
Min length0

Characters and Unicode

Total characters283243
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1252 ?
Unique (%)9.7%

Sample

1st rowDE_Abelein_Manfred_1930
2nd rowDE_Abelein_Manfred_1930
3rd rowDE_Abelein_Manfred_1930
4th rowDE_Abelein_Manfred_1930
5th rowDE_Abelein_Manfred_1930
ValueCountFrequency (%)
de_schaeuble_wolfgang_1942 18
 
0.1%
de_wischnewski_hans_1922 17
 
0.1%
de_jahn_gerhard_1927 17
 
0.1%
de_mischnick_wolfgang_1921 16
 
0.1%
de_barzel_rainer_1924 15
 
0.1%
de_windelen_heinrich_1921 14
 
0.1%
de_riesenhuber_heinz_1935 14
 
0.1%
de_franke_egon_1913 14
 
0.1%
de_strauss_franz_1915 14
 
0.1%
de_wehner_herbert_1906 13
 
0.1%
Other values (4061) 12758
98.8%
2023-11-09T20:50:47.019594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 38730
 
13.7%
e 22255
 
7.9%
r 16450
 
5.8%
1 15366
 
5.4%
9 14162
 
5.0%
E 14083
 
5.0%
D 13650
 
4.8%
a 12462
 
4.4%
n 12212
 
4.3%
i 9768
 
3.4%
Other values (53) 114105
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141210
49.9%
Decimal Number 51651
 
18.2%
Uppercase Letter 51641
 
18.2%
Connector Punctuation 38730
 
13.7%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22255
15.8%
r 16450
11.6%
a 12462
 
8.8%
n 12212
 
8.6%
i 9768
 
6.9%
l 8938
 
6.3%
t 8298
 
5.9%
h 6748
 
4.8%
s 6388
 
4.5%
o 6117
 
4.3%
Other values (16) 31574
22.4%
Uppercase Letter
ValueCountFrequency (%)
E 14083
27.3%
D 13650
26.4%
H 3119
 
6.0%
S 2268
 
4.4%
K 2068
 
4.0%
W 1855
 
3.6%
M 1789
 
3.5%
B 1665
 
3.2%
G 1463
 
2.8%
R 1416
 
2.7%
Other values (15) 8265
16.0%
Decimal Number
ValueCountFrequency (%)
1 15366
29.7%
9 14162
27.4%
4 3799
 
7.4%
3 3114
 
6.0%
2 3062
 
5.9%
5 3055
 
5.9%
8 2417
 
4.7%
0 2416
 
4.7%
6 2356
 
4.6%
7 1904
 
3.7%
Connector Punctuation
ValueCountFrequency (%)
_ 38730
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192851
68.1%
Common 90392
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22255
 
11.5%
r 16450
 
8.5%
E 14083
 
7.3%
D 13650
 
7.1%
a 12462
 
6.5%
n 12212
 
6.3%
i 9768
 
5.1%
l 8938
 
4.6%
t 8298
 
4.3%
h 6748
 
3.5%
Other values (41) 67987
35.3%
Common
ValueCountFrequency (%)
_ 38730
42.8%
1 15366
 
17.0%
9 14162
 
15.7%
4 3799
 
4.2%
3 3114
 
3.4%
2 3062
 
3.4%
5 3055
 
3.4%
8 2417
 
2.7%
0 2416
 
2.7%
6 2356
 
2.6%
Other values (2) 1915
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 38730
 
13.7%
e 22255
 
7.9%
r 16450
 
5.8%
1 15366
 
5.4%
9 14162
 
5.0%
E 14083
 
5.0%
D 13650
 
4.8%
a 12462
 
4.4%
n 12212
 
4.3%
i 9768
 
3.4%
Other values (53) 114105
40.3%
Distinct3824
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size718.7 KiB
2023-11-09T20:50:47.499927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.9412626
Min length0

Characters and Unicode

Total characters64103
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1032 ?
Unique (%)8.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10
ValueCountFrequency (%)
70012 18
 
0.1%
4307 17
 
0.1%
10756 17
 
0.1%
6475 16
 
0.1%
397 15
 
0.1%
10700 14
 
0.1%
7880 14
 
0.1%
2414 14
 
0.1%
9654 14
 
0.1%
70397 13
 
0.1%
Other values (3813) 12478
98.8%
2023-11-09T20:50:48.143350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64030
99.9%
Other Punctuation 73
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13931
21.8%
2 7338
11.5%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%
Other Punctuation
ValueCountFrequency (%)
; 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Interactions

2023-11-09T20:50:21.939821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:53.944095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:05.742479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:15.362213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:23.829041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:31.982899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:38.992751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:46.564854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:55.051382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:03.192203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:11.129030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:19.369835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:28.562255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:37.677663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:42.327847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:51.038750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:56.997963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:22.084057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:54.149780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:06.018147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:15.489819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:23.959782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:32.253530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:39.121516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:46.789710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:55.194819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:03.318424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:11.265016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:19.509355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:28.757590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:37.812445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:42.448530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:51.169946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:49:01.968486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:22.222701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:54.309679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:06.318826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:15.630895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:24.105897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:32.509332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:39.289080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:47.008721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:55.352323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:03.467295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:11.415506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:19.663363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:28.961057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:37.934655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:42.573307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:51.316729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:49:07.010119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:22.368706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:54.514573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:06.492955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:15.749933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:24.245187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:32.797409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:39.427035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:47.161802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:55.487780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:03.588987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:11.542818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:19.790661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:29.149519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:38.059748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:42.698511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:51.452040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:49:12.610103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:22.519920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:54.668750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:06.679775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:15.879161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:24.389329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:33.056698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:39.590067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:47.287912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:55.620314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:03.718621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:11.708999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:19.918844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:29.364982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:38.190546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:42.814840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:48:51.596856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:49:17.302748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:50:22.672211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:46:54.808755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:06.847443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:16.004339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-09T20:47:24.524345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-11-09T20:50:17.573868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-09T20:50:48.338609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
id_de_parliamentelecperyear_birthdistrict_iddistrictvotecloseness_districtlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallpartyid_cmppartyid_chespartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldgenderoffice_spellparty_elecparty_elecdetmandatemandate_detaileddualcandlistministerjunministerparlprescommchairppgchairwhippartyid_bl
id_de_parliament1.0000.6420.6230.077-0.416-0.013-0.054-0.180-0.060-0.146-0.258-0.1120.1260.024-0.2761.0000.6200.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0001.000
elecper0.6421.0000.9210.140-0.414-0.021-0.112-0.204-0.001-0.119-0.185-0.1290.0770.054-0.3020.6420.7590.2890.0690.1870.1830.0560.2200.2720.1260.0510.1000.0000.0860.0400.0321.000
year_birth0.6230.9211.0000.158-0.385-0.019-0.066-0.230-0.039-0.154-0.243-0.1580.0990.034-0.2700.6230.7590.3160.0230.1620.1660.0520.0720.2370.1210.0700.1030.0630.1120.0320.0600.087
district_id0.0770.1400.1581.0000.0290.222-0.035-0.0520.034-0.0710.0690.0470.1520.0820.3010.0770.1410.0750.0000.2250.2220.0080.0140.1800.6410.0360.0220.0300.0320.0240.0330.031
districtvote-0.416-0.414-0.3850.0291.0000.5710.1930.3770.771-0.3070.4870.183-0.1220.1750.474-0.416-0.2360.0000.0000.0260.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
closeness_district-0.013-0.021-0.0190.2220.5711.0000.0260.0340.105-0.1660.1930.0870.2290.0990.205-0.013-0.1060.0510.0000.1180.1120.0620.0360.2750.1390.0450.0040.0000.0410.0000.0000.000
listpos-0.054-0.112-0.066-0.0350.1930.0261.0000.5820.108-0.278-0.1580.074-0.2100.0740.175-0.054-0.0200.1240.0580.1480.1290.2230.1660.1150.2020.1230.0610.0690.0420.1490.0790.172
listpos_total-0.180-0.204-0.230-0.0520.3770.0340.5821.0000.1210.2310.3370.155-0.2060.0370.235-0.180-0.2620.1450.0190.2710.2300.1660.0980.1560.3930.0700.0510.0370.0210.0410.0460.306
elecsafe_district-0.060-0.001-0.0390.0340.7710.1050.1080.1211.000-0.4240.2480.283-0.3350.3030.338-0.060-0.0310.2090.0140.2340.2190.9850.6980.3580.1080.0580.0540.0230.0300.0140.0480.317
elecsafe_list-0.146-0.119-0.154-0.071-0.307-0.166-0.2780.231-0.4241.0000.632-0.2000.058-0.243-0.196-0.146-0.1600.2060.0310.1520.1420.5370.4500.5580.1200.1110.0720.0610.0530.0890.0440.102
elecsafe_overall-0.258-0.185-0.2430.0690.4870.193-0.1580.3370.2480.6321.0000.061-0.1020.0090.173-0.258-0.2530.0900.0330.1350.1280.2930.4960.1420.0960.0950.0900.0540.0850.0700.0160.204
partyid_cmp-0.112-0.129-0.1580.0470.1830.0870.0740.1550.283-0.2000.0611.000-0.4530.7180.751-0.112-0.1680.2720.0370.8560.9990.4280.2200.3630.1230.0810.0480.0270.0190.1270.1151.000
partyid_ches0.1260.0770.0990.152-0.1220.229-0.210-0.206-0.3350.058-0.102-0.4531.000-0.2610.0380.1260.0820.2700.0251.0000.9990.5560.3980.3970.2750.0600.0830.0360.0000.1280.1120.999
partyid_parlgov0.0240.0540.0340.0820.1750.0990.0740.0370.303-0.2430.0090.718-0.2611.0000.7590.024-0.0060.2580.0400.7150.9990.3600.1860.3570.1120.0400.0460.0000.0310.0990.1090.998
partyid_parlgov2-0.276-0.302-0.2700.3010.4740.2050.1750.2350.338-0.1960.1730.7510.0380.7591.000-0.276-0.0270.2110.0641.0000.9990.3020.1610.3100.4800.0650.0400.0380.0260.0410.0371.000
id_de_parliament_string1.0000.6420.6230.077-0.416-0.013-0.054-0.180-0.060-0.146-0.258-0.1120.1260.024-0.2761.0000.6200.8270.0000.8140.7610.6880.5880.6730.7630.1800.0000.2830.2670.2660.3450.200
mp_id_old0.6200.7590.7590.141-0.236-0.106-0.020-0.262-0.031-0.160-0.253-0.1680.082-0.006-0.0270.6201.0000.3100.0390.1500.1480.1350.1430.2690.2920.0830.0650.0450.0730.0250.0510.110
gender0.0000.2890.3160.0750.0000.0510.1240.1450.2090.2060.0900.2720.2700.2580.2110.8270.3101.0000.0150.2770.2770.1800.1800.1070.0970.0260.0000.0220.0270.0200.0490.254
office_spell0.0000.0690.0230.0000.0000.0000.0580.0190.0140.0310.0330.0370.0250.0400.0640.0000.0390.0151.0000.0420.0490.0290.0320.0470.0000.1320.1920.0700.2170.1590.1250.000
party_elec0.0000.1870.1620.2250.0260.1180.1480.2710.2340.1520.1350.8561.0000.7151.0000.8140.1500.2770.0421.0000.9640.4460.2310.3880.2630.0830.0490.0400.0290.1320.1170.999
party_elecdet0.0000.1830.1660.2220.0140.1120.1290.2300.2190.1420.1280.9990.9990.9990.9990.7610.1480.2770.0490.9641.0000.4490.2380.3890.2270.0880.0410.0310.0280.1330.1170.999
mandate0.0000.0560.0520.0080.0000.0620.2230.1660.9850.5370.2930.4280.5560.3600.3020.6880.1350.1800.0290.4460.4491.0001.0000.1610.1710.0550.0410.0180.0430.0000.0320.463
mandate_detailed0.0000.2200.0720.0140.0000.0360.1660.0980.6980.4500.4960.2200.3980.1860.1610.5880.1430.1800.0320.2310.2381.0001.0000.2920.1060.0620.0590.0250.0760.0520.0450.335
dualcand0.0090.2720.2370.1800.0000.2750.1150.1560.3580.5580.1420.3630.3970.3570.3100.6730.2690.1070.0470.3880.3890.1610.2921.0000.3490.0440.0460.0310.0000.0580.0330.329
list0.0000.1260.1210.6410.0000.1390.2020.3930.1080.1200.0960.1230.2750.1120.4800.7630.2920.0970.0000.2630.2270.1710.1060.3491.0000.0640.0210.0380.0370.0460.0750.095
minister0.0000.0510.0700.0360.0000.0450.1230.0700.0580.1110.0950.0810.0600.0400.0650.1800.0830.0260.1320.0830.0880.0550.0620.0440.0641.0000.0330.0130.0420.0310.0260.110
junminister0.0000.1000.1030.0220.0000.0040.0610.0510.0540.0720.0900.0480.0830.0460.0400.0000.0650.0000.1920.0490.0410.0410.0590.0460.0210.0331.0000.0160.0460.0440.0210.200
parlpres0.0000.0000.0630.0300.0000.0000.0690.0370.0230.0610.0540.0270.0360.0000.0380.2830.0450.0220.0700.0400.0310.0180.0250.0310.0380.0130.0161.0000.0430.1720.0120.000
commchair0.0000.0860.1120.0320.0000.0410.0420.0210.0300.0530.0850.0190.0000.0310.0260.2670.0730.0270.2170.0290.0280.0430.0760.0000.0370.0420.0460.0431.0000.0000.0130.000
ppgchair0.0000.0400.0320.0240.0000.0000.1490.0410.0140.0890.0700.1270.1280.0990.0410.2660.0250.0200.1590.1320.1330.0000.0520.0580.0460.0310.0440.1720.0001.0000.0390.017
whip0.0000.0320.0600.0330.0000.0000.0790.0460.0480.0440.0160.1150.1120.1090.0370.3450.0510.0490.1250.1170.1170.0320.0450.0330.0750.0260.0210.0120.0130.0391.0000.050
partyid_bl1.0001.0000.0870.0311.0000.0000.1720.3060.3170.1020.2041.0000.9990.9981.0000.2000.1100.2540.0000.9990.9990.4630.3350.3290.0950.1100.2000.0000.0000.0170.0501.000

Missing values

2023-11-09T20:50:30.389991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-09T20:50:31.348813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-09T20:50:31.979376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_de_parliamentlastnamefirstnameelecpergenderyear_birthdate_birthdate_birth_textmandate_startmandate_endoffice_spellspell_startspell_endparty_elecparty_elecdetmandatemandate_detaileddualcanddistrict_iddistrictvotecloseness_districtlistlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallministerjunministerparlprescommchairppgchairwhippartyid_cmppartyid_chespartyid_blpartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldpers_id_pdbdid_de_manow
011000001.0AbeleinManfred8male19301930-10-2020/10/19301976-12-141980-11-041.01976-12-141980-11-04CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5710.202897NaNNaNNaN0.9824700.0000000.982470nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
111000001.0AbeleinManfred7male19301930-10-2020/10/19301972-12-131976-12-131.01972-12-131976-12-13CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5310.114864NaNNaNNaN0.9495560.0000000.949556nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
211000001.0AbeleinManfred11male19301930-10-2020/10/19301987-02-181990-12-201.01987-02-181990-12-20CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5290.184236NaNNaNNaN0.9913590.0000000.991359nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
311000001.0AbeleinManfred6male19301930-10-2020/10/19301969-10-201972-09-221.01969-10-201972-09-22CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5670.224192NaNNaNNaN0.9803230.0000000.980323nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
411000001.0AbeleinManfred9male19301930-10-2020/10/19301980-11-041983-03-291.01980-11-041983-03-29CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5350.154658NaNNaNNaN0.9635920.0000000.963592nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
511000001.0AbeleinManfred5male19301930-10-2020/10/19301965-10-191969-10-191.01965-10-191969-10-19CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5770.276113NaNNaNNaN0.9912720.0000000.991272nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
611000001.0AbeleinManfred10male19301930-10-2020/10/19301983-03-291987-02-171.01983-03-291987-02-17CDUCDU (Christian Democratic Union)district mandatedistrict mandateno174.00.5850.241266NaNNaNNaN0.9917840.0000000.991784nononononono41521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
711000002.0AchenbachErnst5male19091909-04-0909/04/19091965-10-191969-10-191.01965-10-191969-10-19FDPFDP (Free Democratic Party)list mandatelist mandate (at time of election)yes97.0NaN0.191258Nordrhein-Westfalen7.045.00.0004570.9947210.994723nononononono41420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
811000002.0AchenbachErnst4male19091909-04-0909/04/19091961-10-171965-10-171.01961-10-171965-10-17FDPFDP (Free Democratic Party)list mandatelist mandate (at time of election)yes99.0NaN0.088874Nordrhein-Westfalen6.061.00.0038200.9899410.989979nononononono41420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
911000002.0AchenbachErnst6male19091909-04-0909/04/19091969-10-201972-09-222.01971-12-071972-09-22FDPFDP (Free Democratic Party)list mandatelist mandate (at time of election)yes97.0NaN0.245191Nordrhein-Westfalen9.056.00.0002120.9160570.916075nonononoyesno41420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
id_de_parliamentlastnamefirstnameelecpergenderyear_birthdate_birthdate_birth_textmandate_startmandate_endoffice_spellspell_startspell_endparty_elecparty_elecdetmandatemandate_detaileddualcanddistrict_iddistrictvotecloseness_districtlistlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallministerjunministerparlprescommchairppgchairwhippartyid_cmppartyid_chespartyid_blpartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldpers_id_pdbdid_de_manow
1296311004962.0DahmenJanosch19male19811981-09-0606/09/19812020-11-122021-10-261.02020-11-122021-10-26GreensGreenslist mandatereplacement mandate from listyes139.00.0858640.281263Nordrhein-Westfalen14.039.00.0007430.3427770.343266nononononono41113.0304.0NaN772.0772.011004962NaN
1296411004966.0NordtKristina19female19821982-02-1717/02/19822021-03-222021-10-261.02021-03-222021-10-26CDUCDU (Christian Democratic Union)list mandatereplacement mandate from listnoNaNNaNNaNThüringen6.013.00.0000000.0002720.000272nononononono41521.0301.0NaN1727.0808.011004966NaN
1296511004967.0Friemann-JennertMaika19female19641964-06-2424/06/19642021-04-072021-10-261.02021-04-072021-10-26CDUCDU (Christian Democratic Union)list mandatereplacement mandate from listnoNaNNaNNaNMecklenburg-Vorpommern7.011.00.0000000.0000850.000085nononononono41521.0301.0NaN1727.0808.011004967NaN
1296611004968.0GohlChristopher19male19741974-05-2323/05/19742021-05-012021-10-261.02021-05-012021-10-26FDPFDP (Free Democratic Party)list mandatereplacement mandate from listyes290.00.0790460.277787Baden-Württemberg13.037.00.0003470.2610550.261311nononononono41420.0303.0NaN543.0543.011004968NaN
1296711004969.0EmmerichMarcel19male19911991-05-1212/05/19912021-06-012021-10-261.02021-06-012021-10-26GreensGreenslist mandatereplacement mandate from listyes291.00.1196390.306987Baden-Württemberg16.040.00.0001630.0158960.016056nononononono41113.0304.0NaN772.0772.011004969NaN
1296811004970.0SusanneWetterich19female19561956-04-2121/04/19562021-07-012021-10-261.02021-07-012021-10-26CDUCDU (Christian Democratic Union)list mandatereplacement mandate from listnoNaNNaNNaNBaden-Württemberg11.060.00.0000000.1963960.196396nononononono41521.0301.0NaN1727.0808.011004970NaN
1296911004971.0FlorianJäger19male19711971-01-1818/01/19712021-07-202021-10-261.02021-07-202021-10-26AfDAfD (Alternative für Deutschland)list mandatereplacement mandate from listyes215.00.1022290.334126Bayern15.0NaN0.0000400.1424620.142496nononononono41953.0310.0NaN2253.02253.011004971NaN
1297011004972.0ZekiGökhan19male19561956-02-1212/02/19562021-08-192021-10-261.02021-08-192021-10-26Left Party/PDSLeft/PDS (The Left, previously Party of Democratic Socialism)list mandatereplacement mandate from listyes91.00.0453680.346558Nordrhein-Westfalen14.030.00.0001530.0460130.046159nononononono41222.0306.0NaN791.0791.011004972NaN
1297155555556.0HinzPeter18male19581958-04-1010/04/19582013-10-272013-10-221.02013-10-272013-10-22CDUCDU (Christian Democratic Union)list mandatelist mandate (at time of election)noNaNNaNNaNBaden-Württemberg16.055.00.0000000.1716480.171648nononononono41521.0301.0NaN1727.0808.055555556NaN
1297266666664.0BerndBuchholz19male19611961-11-0202/11/19612017-10-242017-10-241.02017-10-242017-10-24FDPFDP (Free Democratic Party)list mandatelist mandate (at time of election)yes10.00.0822620.312685Schleswig-Holstein2.010.00.0000960.7670550.767078nononononono41420.0303.0NaN543.0543.066666664NaN